In our experiments, we compare the heat maps generated using different tile sizes (\(T_{g}\) ) by models trained on different tile sizes (\(T_{m}\) ) and also models which have been trained normally using common training practices i.e. pretrained and available to download via packages like PyTorch [1]} and TensorFlow [2]} without any additional training. Figure REF  shows the heat maps of ResNet18 models that were trained on tiles of sizes 16x16, 56x56 and on full images of 96x96 of STL-10 dataset, generated using tiles of sizes 16, 24, 32, 48, 56 and 64 on a sample image. (Figure REF  in Appendix REF  shows the heatmap on a randomly downloaded image generated with 64x64 tiles using a mobilenet_v2 pretrained on Imagenet-1k model downloaded using PyTorch's model zoo and without any additional retraining.) These heat maps show that CNNs learn to classify on small tiles whether they were explicitly trained on tiles or not, leading us to theorize that CNNs are only learning the labels of tiles and in general do not care about the global structure of the image, aided by the mitigation of curse of dimensionality, as discussed in Section . For a more comprehensive comparison of heat maps of Figure REF  see the Appendix.
